Systems for determining customer interest in goods

ABSTRACT

A system for determining customer interest in goods includes one or more memory devices storing instructions and one or more processors configured to execute the instructions. The processors are configured to receive customer location data from a smart device associated with a customer indicating the customer is within a retail venue of a retailer and to monitor, based on the customer location data, a current location of the customer within the retail venue. The processors are further configured to receive goods location data indicating locations of goods for sale within the retail venue and determine that the customer is interested in a particular good for sale within the retail venue based on the current customer location remaining in proximity to the location of the particular good for a predetermined period of time. The processors also conduct a search of pricing of the particular good at one or more other retailers and send a price comparison to the customer.

TECHNICAL FIELD

The present disclosure generally relates to a system for determiningcustomer interest in goods.

BACKGROUND

Reliably and seamlessly price checking goods within retail venues is aburdensome task for customers purchasing multiple, or even unique,goods.

As one example, a customer at a brick-and-mortar store location mayselect upwards of 30 items, and place them into a single shopping cart.The customer may wish to conduct an item-by-item price check. However,in order to do so, the customer must manually run the price comparisons,either with a search engine or a smart device application, orautomatically with a scanning system, and hope the information isaccurate. Such a shopping experience can lead to errors, such aspurchasing the wrong good or amount of goods based on a believed deal.

As another example, a particular brick-and-mortar store location maylose customers, who were once very loyal, to other retail venuesoffering more competitive pricing. The other venues can be found online,at other physical locations, or both. The customers may prefer shoppingat the particular brick-and-mortar store location but price comparisonsfor like items they find on the internet, or from a shoppingapplication, uncover competing prices that are too hard to pass up. Thestore owner is unaware of the competing pricing and never has anopportunity to offer a responsive discount in order to retain thecustomers.

Moreover, while some computerized solutions exist for tracking customerproximity to goods, and offering discounts, such solutions typicallystop there. This is inefficient and does not collect and utilize datafor the benefit of both the store owner and the customer.

The present disclosure provides systems and devices to solve these andother problems.

SUMMARY

In the following description, certain aspects and embodiments of thepresent disclosure will become evident. It should be understood that thedisclosure, in its broadest sense, could be practiced without having oneor more features of these aspects and embodiments. Specifically, itshould also be understood that these aspects and embodiments are merelyexemplary. Moreover, although disclosed embodiments are discussed in thecontext of a processor bracket and, it is to be understood that thedisclosed embodiments are not limited to any particular industry.

Disclosed embodiments include a system for determining customer interestin goods. The system comprises one or more memory devices storinginstructions and one or more processors configured to execute theinstructions. The processors are configured to receive customer locationdata from a smart device associated with a customer indicating thecustomer is within a retail venue of a retailer and to monitor, based onthe customer location data, a current location of the customer withinthe retail venue. The processors are further configured to receive goodslocation data indicating locations of goods for sale within the retailvenue and determine that the customer is interested in a particular goodfor sale within the retail venue based on the current customer locationremaining in proximity to the location of the particular good for apredetermined period of time. The processors also conduct a search ofpricing of the particular good at one or more other retailers and send aprice comparison to the customer for the particular good based onresults of the price search.

It is to be understood that both the foregoing general description andthe following detailed description are exemplary and explanatory only,and are not restrictive of the disclosed embodiments, as claimed.

BRIEF DESCRIPTION OF DRAWINGS

The accompanying drawings, which are incorporated in and constitute apart of the specification, illustrate several embodiments and, togetherwith the description, serve to explain the disclosed principles. In thedrawings:

FIG. 1A is a block diagram of an exemplary system, consistent withdisclosed embodiments.

FIG. 1B is a diagram of an exemplary electronic system, consistent withdisclosed embodiments.

FIG. 1C is a diagram of an exemplary electronic system, consistent withdisclosed embodiments.

FIG. 1D is a diagram of an exemplary electronic system, consistent withdisclosed embodiments.

FIG. 2A is a diagram of an exemplary retail venue consistent withdisclosed embodiments.

FIG. 2B is a block diagram of an exemplary system, consistent withdisclosed embodiments.

FIG. 3 is a flowchart of an exemplary process for modeling and analyzingcustomer shopping behavior and purchase determinations.

FIG. 4 is a flowchart of an exemplary process for determining a customerinterest in goods and sending a price comparison to the customer.

FIG. 5 is a flowchart of an exemplary process for determining a customerinterest in goods, sending a price comparison, and updating the customerprofile based on purchase behavior.

FIG. 6 is a flowchart of an exemplary process for determining multiplecustomers interest in goods for multiple affiliated venues.

DETAILED DESCRIPTION

An initial overview of proximity detection technology is providedimmediately below and then specific exemplary embodiments of systems andmethods for determining customer interest in goods are described infurther detail later. The initial overview is intended to aid inunderstanding some of the useful technology relevant to systems andmethods disclosed herein, but it is not intended to limit the scope ofthe claimed subject matter.

One means of proximity detection technology is via communication eitherbetween two devices or communication gathered on a network encompassingtwo devices. Wireless communication is more typical due to the natureand intentions associated with proximity detection (i.e., wiredcommunication likely provides some indication of proximity already). Thewireless communication of proximity based content enables a userassociated with a user device to send or receive content, via a userdevice, when the user device is within a limited proximity of a seconddevice associated with a location or object (e.g., a good for sale). Thecontent may be related to or associated with the location or object.Also, the sending or receiving of the content may be triggered by theuser entering a limited proximity of the location or the object.

Wireless communication is any form of communication between two deviceswhere some point of communication does not require a physical wiredconnection. Some wireless communication is based on radio frequencies,but wireless communication is not limited to the radio frequencies.

In one example, wireless communication and proximity detection can beaccomplished with a user's mobile computing device (e.g., a smartphone).While the mobile computing device is described herein as being mobile,the mobile computing device may be a fixed device. The mobile computingdevice can be a handheld computing device, a wearable computing device,a portable multimedia device, a smartphone, a tablet computing device, alaptop computer, a smart watch, an embedded computing device, or similardevice. An embedded computing device is a computing device that isinlayed in a selected object such as a vehicle, a watch, a key fob, aring, a key card, a token, a poker chip, a souvenir, a necklace amulet,and so forth. A computing device may be embedded in substantially anytype of object. The mobile computing device can be a device that is userowned, rented, leased, associated with, or otherwise in the possessionof the user.

The wireless communication can be between the user's mobile device and asecond proximity device, such as a tag, that is associated with or nearthe object/good. Like the user device, the tag can be fixed or mobile.The tag may be another mobile computing device or another device. Thetag may be owned by the user or another entity.

The location proximity based content that is communicated between theuser device and tag, or routed through a network, may include contentthat is locally stored on each device, content that is received througha wired or wireless network from a remote storage device, or acombination thereof. The communicated content may be generated by theuser or another entity either locally or remotely, and in advance orcontemporaneously with the sending of the content.

Reference will now be made in detail to exemplary embodiments, examplesof which are illustrated in the accompanying drawings and disclosedherein. Wherever convenient, the same reference numbers will be usedthroughout the drawings to refer to the same or like parts.

The disclosed embodiments are directed to systems and methods fordetermining customer interest in goods for sale. While some computerizedsolutions exist for tracking customer proximity to goods, and offeringdiscounts, such solutions typically stop there. This is inefficient anddoes not collect and utilize data for the benefit of both the storeowner and the customer. Furthermore, none of the other solutions utilizemachine learning to properly stock a retail venue, or model purchasingbehavior on micro and macro levels for individual or multiple customersand locations. And there is no system for combining such data todetermine customer interest in goods by analyzing their proximity to agood, their past purchasing behaviors and interests, and market pricingtrends to further determine a competitive price adjustment.

There exists substantial untapped consumer data sources that can beutilized to provide improved services for prospective customers. Onesuch area of underutilized data is in determining customer interest ingoods. In particular, customer interest could be determined based ontheir proximity, and duration of proximity, to specific goods in aphysical retail venue, as well as the association of those specificgoods to other goods in the retail venue. To make this determination, asystem for determining customer interest would need to collect orreceive input data regarding the location and movement of the goods, aswell as the location and movement of the customer. Once the systemcollects or receives data from which to determine a customer interestedin a particular good, then the system could further utilize that data toprovide an improved service, such as facilitating price comparison oroffering reduced pricing for the particular good.

The following description provides examples of systems and methods fordetermining customer interest in goods. The arrangement of componentsshown in the figures is not intended to limit the disclosed embodiments,as the components used in the disclosed systems may vary.

FIG. 1A depicts an illustrative system 100 for determining a customerinterest in goods in accordance with aspects of an embodiment of thepresent disclosure. System 100 includes a customer smart device 110,which can be any user device discussed above, in wireless communicationwith a monitor device 120 which is in further communication with a tagdevice 130 that indicates a physical location of a good for sale withina retail venue. As discussed above, the means of communication betweendevices 110, 120, and 130 can vary and the particular combination canalso vary such that device 110 may communicate directly with device tag130 and vice versa. Monitor device 120 further communicates with anetwork 140. It will also be understood that devices 110, 120, and 130may also communicate directly with network 140 or through network 140.Customer smart device 110, monitor device 120, tag device 130, andnetwork 140 further communicate with a storage device 150. Storagedevice 150 stores an information model 160 and a customer profile 170.

Through these illustrative components, system 100 collects and utilizesdata for the benefit of both the store owner and the customer. Forinstance, by collecting location and proximity data with devices 110,120, and 130, storage device 150 can further analyze customer interestsin prospective goods with model 160. A store owner may further use model160 analysis and historical data stored in customer profile 170 to offera competitive service, through price adjustments, product placements,product pairings, etc., for the customer. The customer, for benefitingfrom this beneficial experience, will in turn stay loyal to the storeowner.

FIG. 1B illustrates an exemplary configuration of smart device 110,consistent with disclosed embodiments. Variations of smart device 110may be used to implement portions or all of each of the devices ofsystem 100, such as monitor device 120, good tag 130, and storage device150. Likewise, even though FIG. 1B depicts smart device 110, it isunderstood that devices 120, 130 and 150 may implement portionsillustrated by exemplary smart device 110. As shown, smart device 110includes a display 111, an input/output (“I/O”) device 112, one or moreprocessors 113, and a memory 114 having stored therein one or moreprogram applications 115, such as an account app 116, and data 117.Smart device also includes an antenna 118 and one or more sensors 119.One or more of display 111, I/O devices 112, processor(s) 113, memory114, antenna 118, or sensor(s) 119 may be connected to one or more ofthe other devices depicted in FIG. 1B. Such connections may beaccomplished using a bus or other interconnecting device(s).

Processor 113 may be one or more known processing devices, such as amicroprocessor from the Pentium™ or Atom™ families manufactured byIntel™, the Turion™ family manufactured by AMD™, the Exynos™ familymanufactured by Samsung™, or the Snapdragon™ family manufactured byQualcomm™. Processor 113 may constitute a single core or multiple coreprocessors that executes parallel processes simultaneously. For example,processor 113 may be a single core processor configured with virtualprocessing technologies. In certain embodiments, processor 113 may uselogical processors to simultaneously execute and control multipleprocesses. Processor 113 may implement virtual machine technologies, orother known technologies to provide the ability to execute, control,run, manipulate, store, etc., multiple software processes, applications,programs, etc. In another embodiment, processor 113 may include amultiple-core processor arrangement (e.g., dual, quad core, etc.)configured to provide parallel processing functionalities to allow smartdevice 110 to execute multiple processes simultaneously. One of ordinaryskill in the art would understand that other types of processorarrangements could be implemented that provide for the capabilitiesdisclosed herein.

I/O devices 112 may include one or more devices that customer smartdevice 110 to receive input from a customer and provide feedback to thecustomer. I/O devices 112 may include, for example, one or more buttons,switches, speakers, microphones, or touchscreen panels. In someembodiments, I/O devices 112 may be manipulated by the customer 105 toinput information into smart device 110.

Memory 114 may be a volatile or non-volatile, magnetic, semiconductor,tape, optical, removable, non-removable, or other type of storage deviceor tangible (i.e., non-transitory) computer-readable medium that storesone or more program applications 115 such as account app 116, and data117. Data 117 may include, for example, customer personal information,account information, and display settings and preferences. In someembodiments, account information may include items such as, for example,an alphanumeric account number, account label, account balance, accountissuance date, account expiration date, account issuer identification, agovernment ID number, a room number, a room passcode, and any othernecessary information associated with a customer and/or an accountassociated with a customer, depending on the needs of the customer,entities associated with network 140, and/or entities associated withsystem 100.

Program applications 115 may include operating systems (not shown) thatperform known operating system functions when executed by one or moreprocessors. By way of example, the operating systems may includeMicrosoft Windows™, Unix™, Linux™, Apple™, or Android™ operatingsystems, Personal Digital Assistant (PDA) type operating systems, suchas Microsoft CE™, or other types of operating systems. Accordingly,disclosed embodiments may operate and function with computer systemsrunning any type of operating system. Smart device 110 may also includecommunication software that, when executed by processor 113, providescommunications with network 140, such as Web browser software, tablet,or smart hand held device networking software, etc. Smart device 110 maybe a device that executes mobile applications for performing operationsconsistent with disclosed embodiments, such as a tablet, mobile device,or smart wearable device.

Program applications 115 may include account app 116, such as an accountapp for activating, setting up, and configuring a customer access tocommunication with devices 120, 130, and 150 through the customeraccount. In some embodiments, account app 116 may include instructionswhich cause processor 111 to connect to monitor device 120, good tag130, and/or storage device 150 via network 140.

Smart device 110 may also store data 117 in memory 114 relevant to theexamples described herein for system 100. One such example is thestorage of device 110 location proximity to goods data, obtained fromsensors 119, for smart device 110, or alternatively, received frommonitor device 120, and/or tag device 130. Data 117 may contain any datadiscussed above relating to the wireless communication of proximitybased determinations. The data 117 may be further associated withinformation for a particular customer or multiple customers.

Sensors 119 may include one or more devices capable of sensing theenvironment around smart device 110 and/or movement of smart device 110.In some embodiments, sensors 119 may include, for example, anaccelerometer, a shock sensor, a gyroscope, a position sensor, amicrophone, an ambient light sensor, a temperature sensor, and/or aconductivity sensor. In addition, sensors 119 may include devices fordetecting location, such as, a Global Positioning System (GPS), a radiofrequency triangulation system based on cellular or other such wirelesscommunication and/or other means for determining device 110 location.

Antenna 118 may include one or more devices capable of communicatingwirelessly. As per the discussion above, one such example is an antennawirelessly communicating with network 140 via cellular data or Wi-Fi.Antenna 118 may further communicate with monitor device 120, tag device130, or directly with storage device 150 through any wired and wirelessmeans.

FIG. 1C shows an exemplary tag device 130 consistent with disclosedembodiments. Tag device 130 may include components that may execute oneor more processes to determine proximity and location via a processor131. Device 130 may further communicate with monitoring device 120 vianear-field communication (NFC), Wi-Fi, Bluetooth, cellular, and/or othersuch forms of wireless communication discussed herein. In certainembodiments, tag device 130 may include a power supply, such as arechargeable battery, configured to provide electrical power to one ormore components of tag device 130, such as processer 131, a memory 132,and a communication device 133. Alternatively, device 130 may notinclude a power supply and, rather, communicate through passive RFID orother non-powered tag technology. In this non-powered instance, tagdevice 130 may only transmit data when it receives ambient energytransmitted by smart device 110 (e.g., emitting a signal after receivingenergy from radio waves generated by smart device 110). Thus, inembodiments where tag device 130 is a non-powered tag, device 130 mayreceive electromagnetic energy from smart device 110 and use that energyto transmit data stored in tag device 130. Tag devices 130 in someembodiments, may be attached to or otherwise associated with goods. Eachtag device 130 may include a unique identifier and/or other informationidentifying an item to which a tag is attached. In some embodiments, tagdevices 130 may be implemented as Bluetooth Low Energy (BLE) tags. Tagdevices 130 may also include sensors such as temperature sensors, weightsensors, motion sensors, location sensors, proximity sensors,accelerometers, or the like.

In some embodiments, tag devices 130 may be further associated withgoods located at specific locations throughout retail venue 100. Tagdevices 130 may further communicate with monitoring device 120, network140, and storage device 150. Network 140 and/or storage device 150 canstore the mapped specific locations of tag devices 130. In addition, theretail venue itself can be mapped, stored on network 140 or in storagedevice 150, such that system 100 provides directions to customer smartdevice 110. The directions may be to tag devices 130 of interest, or togeneral features of the retail venue (such as exits, checkouts, changingrooms, bathrooms, etc.). In addition, the mapped locations may be basedon tag devices 130, or alternatively, to locations the goods themselves.The system 100 can locate smart device 110 within the retail venue,through network 140 or monitoring device 120, and network 140 (and/ormonitoring device 120) can further monitor smart device 110 locationrelative to tag devices 130. This tag device 130 mapping data may befurther associated with the communicated data from tag devices 130 tomonitor their locations, and in turn, further used to determine tagdevices 130 proximity to smart device 110. As smart device 110 comeswithin proximity to tag devices 130, system 100 can provide smart device110 with good's information associated with tag device 130. And as thesmart device 110 moves about the retail venue, the system 100 canprovide updated tag device 130 information to smart device 110 based ontheir respective proximities.

Returning to FIG. 1A, network 140 may comprise any type of computernetworking arrangement used to exchange data. For example, network 140may be the Internet, a private data network, virtual private networkusing a public network, and/or other suitable connection(s) that enablessystem 100 to send and receive information between the components ofsystem 100. Network 140 may also include a public switched telephonenetwork (“PSTN”) and/or a wireless network such as a cellular network,WiFi network, or other known wireless network capable of bidirectionaldata transmission. Network 140 may also comprise any local computernetworking used to exchange data in a localized area, such as WiFi,Bluetooth™Ethernet, Radio Frequency, and other suitable networkconnections that enable components of system 100 to interact with oneanother.

FIG. 1D shows an exemplary configuration of storage device 150consistent with disclosed embodiments. Variations of exemplary device150 may be used to implement portions or all of devices of system 100,such as smart device 110, monitor device 120, tag device 130, andnetwork 140. Likewise, even though FIG. 1D depicts storage device 150,it is understood that devices 110, 120, and 130 may implement portionsillustrated by exemplary storage device 150. In one embodiment, storagedevice 150 may optionally include one or more processors 151, one ormore input/output (I/O) devices 152, and one or more memories 153. Insome embodiments, device 150 may take the form of a server, generalpurpose computer, mainframe computer, or the like. In some embodiments,device 150 may take the form of a mobile computing device such as asmartphone, tablet, laptop computer, or the like.

Alternatively, device 150 may be configured as a particular apparatus,embedded system, dedicated circuit, or the like, based on the storage,execution, and/or implementation of the software instructions thatperform one or more operations consistent with the disclosedembodiments.

Processor(s) 151 may include one or more known processing devices, suchas mobile device microprocessors, desktop microprocessors, servermicroprocessors, or the like. The disclosed embodiments are not limitedto a particular type of processor.

I/O devices 152 may be one or more devices configured to allow data tobe received and/or transmitted by device 150. I/O devices 152 mayinclude one or more digital and/or analog devices that allow storagedevice 150 to communicate with other machines and devices, such as othercomponents and devices of system 100. For example, I/O devices 152 mayinclude a screen for displaying messages to a user (such as a customeror retail venue manager). I/O devices 152 may also include one or moredigital and/or analog devices that allow a user to interact with system100, such as a touch-sensitive area, keyboard, buttons, or microphones.I/O devices 152 may also include other components known in the art forinteracting with a user. I/O devices 152 may also include one or morehardware/software components for communicating with other components ofsystem 100. For example, I/O devices 152 may include a wired networkadapter, a wireless network adapter, a cellular network adapter, or thelike. Such network components enable device 150 to communicate withother devices of system 100 to send and receive data.

Memory 153 may include one or more storage devices configured to storeinstructions usable by processor 151 to perform functions related to thedisclosed embodiments. For example, memory 153 may be configured withone or more software instructions, such as one or more programapplications 154 that perform one or more operations when executed byprocessor 151. The disclosed embodiments are not limited to separateprograms or computers configured to perform dedicated tasks. Forexample, memory 153 may include a single program or multiple programsthat perform the functions of mobile device 110, good monitor device120, or tag device 130. Memory 153 may also store data 155 that is usedby the one or more applications 154.

In certain embodiments, memory 153 may store software executable byprocessor 151 to perform one or more methods, such as the methodsrepresented by the flowcharts depicted in FIGS. 3-6 and/or the methodsassociated with user interface (e.g., display 111) discussed above withreference to FIG. 1B. In one example, memory 153 may store one or moreprogram applications 154. Applications 154 stored in memory 153, andexecuted by processor 151, may include a venue app that causes processor151 to execute one or more processes related to financial servicesprovided to customers including, but not limited to, processing creditand debit card transactions, checking transactions, processing paymentsfor goods, price checking goods, analyzing customer purchasing behaviorand adjusting good pricing based on the analysis, and/or adjusting goodpricing. In some examples, program applications 154 may be stored in anexternal storage device, such as a cloud server located outside ofnetwork 140, and processor 151 may retrieve and execute the externallystored programs 154.

Storage Device 150 may be used to store data 155 relevant to examplesdescribed herein for system 100. One such example is the storage oflocation proximity data received from smart device 110, monitor device120, or tag device 130. Data 155 may contain any data discussed aboverelating to the wireless communication of proximity baseddeterminations. In addition, data 155 may contain customer profile 170data such as purchasing behavior determinations, previous purchasingpatterns, inventory listing of goods for sale, goods price, pricecomparison data, previous offered discounts for goods. The data 155associated with particular customer or retail venue may also containassociated information for customers or retail venues. Data 155 mayfurther include data unique for each good tag, as well as anyinformation relative to any particular good. Data 155 may also includemodel 160 determinations and analysis.

Storage device 150 may include at least one database 156. Database 156may be a volatile or non-volatile, magnetic, semiconductor, tape,optical, removable, nonremovable, or other type of storage device ortangible (i.e., non-transitory) computer readable medium. For example,database 156 may include at least one of a hard drive, a flash drive, amemory, a Compact Disc (CD), a Digital Video Disc (DVD), or a Blu-ray™disc.

Database 156 may store data, such as data 155 that may be used byprocessor 151 for performing methods and processes associated withdisclosed examples. Data stored in database 156 may include any suitabledata, such as information relating to a customer, and/or a retail venue,information relating to transactions, and information model 160 and/orcustomer profile 170, data relating to the customer determinations, ormodeled purchasing behavior. Although shown as a separate unit in FIG.1D, it is understood that database 156 may be part of memory 153, or anexternal storage device located outside of system 100. At least one ofmemory 153, and/or database 156 may store data and instructions used toperform one or more features of the disclosed examples. At least one ofmemory 153, and/or database 156 may also include any combination of oneor more databases controlled by memory controller devices (e.g.,server(s), etc.) or software, such as document management systems,Microsoft SQL databases, Share Point databases, Oracle™ databases,Sybase™ databases, or other relational databases. Storage device 150 mayalso be communicatively connected to one or more remote memory devices(e.g., databases (not shown)) through network 140, or a differentnetwork. The remote memory devices may be configured to storeinformation and may be accessed and/or managed by system 100. Systemsand methods consistent with disclosed examples, however, are not limitedto separate databases or even to the use of a database.

The components of device 150 may be implemented in hardware, software,or a combination of both hardware and software, as will be apparent tothose skilled in the art. For example, although one or more componentsof device 150 may be implemented as computer processing instructions,all or a portion of the functionality of device 150 may be implementedinstead in dedicated electronics hardware.

Storage device 150 also stores model 160 and customer profile 170.Through processor(s) 151, storage device 150 runs model 160 forperforming methods and processes associated with disclosed examplesdescribed more fully below. Model 160 may analyze received data 155 forcustomers including, but not limited to, processed transactions, checkedtransactions, checked goods prices at third party retailer, processedpayments for goods, purchasing customer behavior, and/or adjusted goodpricing. In some examples, model 160 may be stored in an externalstorage device, such as a cloud server located outside of network 140and storage device 150, and processor 151 may execute the model 160remotely.

Customer profile 170 is a subset of data 155 stored in device 150 andanalyzed by model 160. Data 155 is further associated with multiplecustomers and each respective customer has a customer profile thatcontains their associated purchasing behavior determinations andanalysis.

FIG. 2A illustrates an exemplary retail venue 200 system. Upon entryinto retail venue 200, a customer 205 moves about the physical premises.Customer 205 has smart device 110 that communicates with monitoringdevice 120 and tag devices 130 a-n, associated with respective goods 232a-n, as well as storage device 150. Smart device 110 transmits data 206within venue 200 and accesses network 140. Tag devices 130 a-n transmitdata 208 a-n to monitoring device 120, and/or smart device 110, and/orvia network 140 to storage device 150. Monitoring device 120 receivesdata 209 from smart device 110 and tag devices 130 a-n and furtherroutes the data to storage device 150. Communication between smartdevice 110, monitoring device 120, and tag devices 130 may occur throughvarious means. Some forms of communication, as already discussed, arenear-field communication (NFC), Wi-Fi, Bluetooth, cellular, and/or othersuch forms of wireless communication discussed herein. In certainembodiments, smart device 110 and/or tag device 130 may include a powersupply, such as a battery, configured to provide electrical power to oneor more components of smart device 110 and/or tag device 130, such asprocesser 113/131, a memory 114/132, and a communication device 118/133.Alternatively, 130 may not include a power supply and, rather,communicate through passive RFID or other non-powered tag technology. Inthis non-powered instance, tag device 130 may only transmit data when itreceives ambient energy transmitted by another device, such as smartdevice 110 or monitoring device 120(e.g., tag device 130 emitting asignal after receiving energy from radio waves generated by smart device110 or monitoring device 120). Thus, in embodiments where device 130 isnon-powered, tag device 130 may receive electromagnetic energy fromanother device and use that energy to transmit data stored within.Alternatively, smart device 110 and/or tag device 130 may store locationproximity data within an internal memory component (i.e., memory 114and/or 132), or the devices 110 and/or 130 may continuously transmittheir location data to monitoring device 120.

FIG. 2A depicts a wired connection between monitoring device 120 andstorage device 150, but it is further understood that this connection ispossible either through wired or wireless communication means asdiscussed throughout here.

A person of ordinary skill will now understand that the retail venue 200system and good 232 a-n placement throughout the retail venue can bealtered to better suit the store owner and customer. For instance, basedon collected data and/or model 160, popular items may be relocated nearthe check-out area or by the entrance to catch the attention of customer205. Alternatively, goods 232 a-n can be placed near each other based onpast determined interests in prospective goods.

In particular, FIG. 2A illustrates product tag device 130 a located on avenue shelf adjacent to good 232 a (i.e., a TV set in FIG. 2A). Tagdevice 130 a is configured to transmit signal 208 a with enough power sothat signal 208 a is detectable within venue 200, and in particular, bymonitoring device 120. Customer 205 carries smart device 110, which isconfigured to receive signal 208 a from tag device 130 a and/or transmitits own signal 206, along with collected proximity data to tag devices130 a-n, which is further detected by monitoring device 120 or routed tonetwork 140 (and back to storage device 250) by other means. It isfurther understood that smart device 110 may receive and transmitsignals, and it may also by-pass monitoring device 120. Signals 206 and208 a-n are collected by storage device 150, either through network 140or monitoring device 120, and customer profile 170 further determinescustomer 205 locations relative to goods tags 130 a-n.

FIG. 2B shows network 140 may be further associated with an affiliatedretailer network 240 including retail venues 200 a-200 e. Storagedevices associated with venues 200 a-200 e, connected through affiliatedretailer network 240, collect multiple customer profiles 170 formultiple customers 205 at venue 200 a, as well as, similar data fromvenues 200 b-e. Affiliated retailer network 240 may further gather modelanalysis from each of the venues 200 a-e and store the gathered analysisat any storage device 150 in the affiliated venues 200 a-e. Affiliatedretailer network 240 further provides communication between venues 200a-e through network 140.

FIG. 3 is a is a flow chart of an exemplary process for modeling, tobuild model 160, collected data from venues 200 a-e, tag devices 130a-n, and smart devices 110. The process begins by collecting input 320such as customers' accounts 302, customers' interests 304, customers'location data 306, goods location data 308, and customers' purchasehistory 310. As discussed above, customers' profiles 302 contain datacollected by monitoring devices 120 and storage devices 150 acrossrespective venues 200 a-e. The customers' profiles may contain the abovementioned data collected at step 320, such as customers' accounts 302,customers' interests 304, customer location data 306 in the retailvenues, associated goods data 308 within proximity to each customer, andcustomers' purchase history 310 at each venue 200 a-e, but thecustomers' profiles generally include the determined analysis ofcustomers' interests in purchasing select goods, as determined by model160. A customer's account 302 may be a customer configured profileaffiliated with the venues 200 a-e. The customer may further configureaccount 302 to provide secure access to the customer's purchasinginformation and shipping information. And the customer may furthercustomize a shopping list on account 302. Each customer's interests 304may be a collection of pre-selected interests of the customer fromeither smart device 110 or customer account 302. For example, customer205 may update its account 302 with fruit produce brand preferences andthis information may be further routed via network 140 to model 300 (atstep 320) to determine future interests. Alternatively, customer 205,via smart device 210 or similar device, may notify retail venue 200, vianetwork 140, of an intended shopping list from account 302, and customerinterest 304 information containing the shopping list from account 302will also transmitted to model 300. Location data 306 and 308 willcontinuously be monitored, while the respective smart device 110 andgood tag devices 130 a-n, are within retail venues 200 a-e. Thislocation data contains proximity data and time durations. For instance,this location data may contain proximity distance data between smartdevice 110 and good tag devices 130 a-n, as well as, time duration dataindicating how long device 110 and good tag devices 130 a-n were withinproximity to each other. Model 300 also receives, at step 320,customers' purchase history 310 from venues 200 a-e. Purchase historydata may include information for every good purchase in venues 200 a-e,by customer 205, as well as the pricing for each good, the discountedoffers for each good, the price checked comparison for each good, theadjusted prices for each good, and even the rejected goods customer 205decided not to purchase (after specifically being offered a discount orafter it was determined customer 205 would purchase the good).

Next, at step 330, model 300 is updated with the newly received datafrom step 320. Steps 320 and 330 are performed in real time and model300 continuously receives data and updates itself based on the new data.Then at step 340, model 300 analyzes the received data 320 to determinemicro and macro purchasing patterns for specific customers and thecollective customers for all venues 200 a-e. Model 300 may employvarious machine learning techniques to analyze the collected data 320.Examples of machine learning techniques include decision tree learning,association rule learning, artificial neural networks, inductive logicprogramming, support vector machines, clustering, Bayesian networking,reinforcement learning, representation learning, similarity and metriclearning, spare dictionary learning, rule-based machine learning, etc.For example, at step 340, model 300 may analyze the proximity data andtime duration data received in step 320 and determine that customer 205is interested in certain goods because device 110 was within proximityto good tag devices 130 a-n for a set amount of time (e.g., threeminutes). And as model 300 learns, from above techniques, this timeduration trigger may adjust such that customer 205's proximity to goodsfor less than three minutes may also indicate an interest in the goods.

A person of ordinary skill will now understand that through thesemodeling steps, system 100 further facilitates the goal of trackingcustomer proximity to goods and offering an improved retail shoppingexperience. By utilizing customer and good location data, and machinelearning, model 300 may further assist the store owner by providinganalytics to properly stock the retail venue, and track purchasingtrends at micro and macro levels. The analytics can determine accurateshopping trends to enable the retail venue owner to negotiate favorablepurchases, on the supply side, and in return, offer favorable retailpricing on the demand side.

FIG. 4 is a flowchart of an exemplary process for determining customer205 interest in goods 232 a-n in retail venue 200, any one of goods 232a-n more generally referred to therein as goods 232. The process beginsat step 401, where monitoring device 120 enters scanning mode, wherebyit detects and receives customer 205 location data from venue 200,either by direct communication between smart device 110 and device 120or through network 140. At step 402, monitoring device 120 continuouslyreceives data from smart device 110 and monitors the customer 205(associated with device 110) locations within venue 200. In addition,monitoring device 120 continuously scans for signals from tag devices130 a-n as well.

At step 403, monitoring device 120 receives associated goods 232locations from tag devices 130 a-n within venue 200. Monitoring devicemay scan for tag devices 130 a-n in particular zones within venue 200.In some embodiments, tag devices 130 a-n may begin transmitting data instep contemporaneously with step 403 when smart device 110 is detectedwithin proximity. For example, where device 130 is implemented as aBluetooth Low Energy tag, tag device 130 may transmit data at the end ofa time interval (e.g., such as every 500 ms). In embodiments where tagdevice 130 is a powered tag, step 403 may represent a periodic sendingof data by tag device 130. In other embodiments, such as those wherepassive RFID or other non-powered tags are used, tag device 130 may onlytransmit data when it receives ambient energy transmitted by smartdevice 110 (e.g., emitting a signal after receiving energy from radiowaves generated by smart device 110). Thus, in embodiments where tagdevice 130 is a non-powered tag, step 403 may represent device 130receiving electromagnetic energy from smart device 110 and using thatenergy to transmit data stored in tag device 130.

In step 404, system 100 determines customer 205 interest in goods 232 byanalyzing the received location data of goods 232 and customer 205location data, and as a duration of customer 205 lingering in proximityto goods 232. System 100 may analyze the received data and deducecustomer interest by triggers other than proximity and duration, forinstance, such as noting the particular good is listed on the account ofcustomer 205.

At step 405, system 100 conducts a price search for goods 232 determinedto be of interest to customer 205 in step 404. Prior to customer 205checking out, system 100 will check for lower prices of goods, either ingoods held by customer 205, or in goods determined to be of interest tocustomer 205. System 100 will compare the current pricing at retailvenue 200 against other non-affiliated retail venues elsewhere, eitherphysically nearby or online. System 100 will further notify customer 205of the results of this price comparison, at step 406, via network 140and smart device 110.

FIG. 5 is a flowchart of an exemplary process for determining theinterest of customer 205 in goods 232 a-n with tag devices 130 a-n andsmart device 110 in retail venue 200. The process begins with step 501,where monitoring device 120 enters scanning mode, by detectingtransmitted signals 206 and 208, and receives location data of customer205 from venue 200, either by direct communication between smart device110 and device 120 or through network 140. At step 502, monitoringdevice 120 continuously receives data from smart device 110 and monitorscustomer 205 (associated with device 110) locations within venue 200. Inaddition, monitoring device 120 continuously scans for signals from tagdevices 130 a-n.

At step 503, monitoring device 120 receives associated goods (232 a-n)locations from tag devices 130 a-n within venue 200. Monitoring devicemay scan for tag devices 130 a-n in particular zones within venue 200.In some embodiments tag devices 130 a-n may begin transmitting data instep contemporaneously with step 503 when smart device 110 is detectedwithin proximity. For example, where device 130 is implemented as aBluetooth Low Energy tag, tag device 130 may transmit data at the end ofa time interval (e.g., such as every 500 ms). In embodiments where tagdevice 130 is a powered tag, step 503 may represent a periodic sendingof data by tag device 130. In other embodiments, such as those wherepassive RFID or other non-powered tags are used, tag device 130 may onlytransmit data when it receives ambient energy transmitted by smartdevice 110 (e.g., emitting a signal after receiving energy from radiowaves generated by smart device 110). Thus, in embodiments where tagdevice 130 is a non-powered tag, step 503 may represent tag device 130receiving electromagnetic energy from smart device 110 and using thatenergy to transmit data stored in tag device 130.

In step 504, system 100 determines customer 205 interest in goods 232 byanalyzing the received location data of goods 232 and customer 205location data, and a duration of customer 205 lingering in proximity togoods 232. System 100 may analyze the received data and deduce customerinterest by triggers other than proximity and duration, for instance,such as noting the particular good is listed on the account of customer205. At step 505, system 100 stores step 504 determinations for customer205 in storage device 150. Based on the stored determinations andreceived data from steps 501-504, system 100 generates customer profile170 in step 506. The generated profile 170 generally containsinformation used to deduce the customer 205 shopping behavior. Forinstance, and as discussed above with reference to FIG. 3, the generatedprofile 170 may contain customer interests, customer location data inthe retail venue, associated goods data within proximity to customer,and customer purchase history at venue 200, but profile 170 generallyincludes of the determined analysis of customer's interest in purchasingselect goods.

At step 507, system 100 generates model 160 with profile 170, locations,and shopping behavior data for customer 205. Like model 300, thegenerated model 160 from step 507 will analyze the shopping trend,behavior, and purchase determinations of customer 205.

At step 508, system 100 conducts a price search for the goods 232 inwhich it was determined that the customer 205 has interest at step 504.Prior to customer 205 checking out, system 100 will check for lowerprices of goods, either in goods held by customer 205, or in goodsdetermined to be of interest to customer 205. System 100 will comparethe current pricing at retail venue 200 against other non-affiliatedvenues elsewhere, either physically or online.

System 100 will further notify customer 205 of the results of this pricecomparison, at step 509, via networks 140 and smart device 210.

At step 510, system 100 receives indication whether or not customer 205purchased goods 232. Not only will system 100 receive indication of allactual purchased goods by customer 205, but it will also receiveindication whether customer 205 purchased goods subject to the step 509price comparison. Monitoring device 120 further communicates, to storagedevice 150, the location data from steps 501-503 and received financialtransaction data such that processor(s) 151 further deduces what goods232 a-n were purchased by customer 205. If it is further determined thatthe good(s) subject to the step 509 price comparison was not purchased,then system 100 further determines price adjustments at step 520. Forexample, if system 100 determines that customer 205 continues to beinterested in good 232 but fails to purchase good 232 after multipletrips to venue 200, then system may determine a new favorable price forgood 232 to incentivize future purchase. The price adjustment at step520 may be for just a particular goods 232, or for collective goods 232a-n, in the form of a future rebate or price reduction offer.

System 100 then updates the customer purchase behavior profile at step530. If system 100 receives indication that customer 205 purchased good232 then at step 530, system 100 updates the customer purchase behaviorprofile. Alternatively, if system 100 receives indication that customer205 did not purchase good 232, even after receiving a future rebate orprice reduction offer at step 520, then at step 530, system 100 updatesthe customer purchase behavior profile.

FIG. 6 is a flowchart of an exemplary process for determining theinterest of multiple customers 205 in goods 232 a-n associated with goodtag devices 130 a-n across affiliated network 140, where each of thesemultiple customers 205 has one of smart devices 110 at retail venues 200a-200 e. The process begins at step 601, where monitoring device 120 ateach retail venue 200 a-200 e enters scanning mode, detectingtransmitted data signals 206 and 208, and receives location data ofcustomer 205 from venues 200 a-e via either direct communication betweensmart devices 110 and each monitoring device 120 or through network 140.At step 602, monitoring device 120 continuously receives data frommultiple smart devices 110 and monitors locations of multiple customers205 (associated with devices 110) within venues 200 a-e. In addition,monitoring device 120 continuously scans for signals from tag devices130 a-e.

At step 603, monitoring device 120 receives associated goods (232 a-n)locations from tag devices 130 a-n within venues 200 a-e. Monitoringdevice 120 may scan for tag devices 130 a-n in particular zones withinvenues 200 a-e. In some embodiments tag devices 130 a-n may begintransmitting data in step contemporaneously with step 603 when smartdevice 110 is detected within proximity. For example, where tag device130 is implemented as a Bluetooth Low Energy tag, tag device 130 maytransmit data at the end of a time interval (e.g., such as every 500ms). In embodiments where tag device 130 is a powered tag, step 603 mayrepresent a periodic sending of data by tag device 130. In otherembodiments, such as those where passive RFID or other non-powered tagsare used, tag device 130 may only transmit data when it receives ambientenergy transmitted by smart device 110 (e.g., emitting a signal afterreceiving energy from radio waves generated by smart device 110). Thus,in embodiments where device 130 is a non-powered tag, step 603 mayrepresent tag device 130 receiving electromagnetic energy from smartdevice 110 and using that energy to transmit data stored in device 130.

In step 604, system 100 determines customer 205 interest in goods 232 byanalyzing the received location of goods data 232 and customer 205location data, and a duration of customer 205 lingering in proximity togood 232. System 100 may analyze the received data and deduce customerinterest by triggers other than proximity and duration, for instance,such as noting the particular good is listed on the account of customer205. At step 605, system 100 stores step 604 determinations for customer205 in storage device 150. Based on the stored determinations andreceived data from steps 601-604, system 100 generates customersprofiles 170 for each respective customer in step 606. The generatedprofiles 170 generally contain information used to deduce the particularcustomer 205 shopping behavior. For instance, and as discussed abovewith reference to FIG. 3, the generated profiles may contain customerinterests, customer location data in the retail venues, associated goodsdata within proximity to customer, and customers purchase history ateach venue 200 a-e, but generally consist of the determined analysis ofcustomer's interest in purchasing select goods.

At step 607, storage device 250 compiles the generated profiles 170.Step 607 may occur on a micro level for each venue 200 a-e or on a macrolevel for all venues. Likewise, step 607 may occur only for a specificcustomer 205 or group of customers. At step 608, system 100 generates amodel with the profiles, locations, and shopping behaviors data forcustomers 205. Like the compiled profiles, one collective model may begenerated for all venues 200 a-e and all customers, or specific modelsmay be created for specific ones of venues 200 a-e and even one specificcustomer 205 Like model 300, the models generated in 608 will analyzethe shopping trends, behavior, and purchase determinations of customers205.

At step 609, system 100 conducts a price search for goods 232 in whichit was determined that the customers 205 have interest at step 604.Prior to each customer 205 checking out, system 100 will check for lowerprices of goods, either in goods held by customer 205, or in goodsdetermined to be of interest to customer 205. System 100 will comparethe current pricing at the retail venue where customer 205 is located,e.g., retail venue 200 a, against other local retail venues 200 b-e ornon-affiliated retail venues elsewhere, either physically nearby oronline. System 100 will further notify customer 205 of the results ofthis price comparison, at step 610, via network 140, and smart device110.

At step 611, system 100 receives indication whether or not customer 205purchased goods 232. Not only will system 100 receive indication of allactual purchased goods by customer 205, but it will also receiveindication whether customer 205 purchased goods subject to the step 610price comparison. Monitoring device 120 further communicates, to storagedevice 150, the location data from steps 601-603 and received financialtransaction data that processor(s) 151 further deduces what goods 232a-n were purchased. If it is further determined that the good(s) subjectto the step 610 price comparison was not purchased, then system 100further determines price adjustments at step 620. For example, ifmultiple customers 205 decide not to purchase a particular good 232after price comparison step 610, then system 100 may determine that amore competitive price is required. Alternatively, if system 100determines that a particular one of customers 205 continues to beinterested in good 232 but fails to purchase good 232 after multipletrips to venue 200, then system 100 may determine a new favorable pricefor good 232 to incentivize future purchase. These price adjustmentdeterminations may be across all retail venues 200 a-e, or at just oneretail venue 200. The price adjustment at step 620 may be for just theparticular good 232 and further just for the particular customer 205 inthe form of a future rebate or price reduction offer.

System 100 then updates the customer purchase behavior profile at step630. If system 100 receives indication that customer 205 purchased good232, then at step 630, system 100 updates the customer purchase behaviorprofile. Alternatively, if system 100 receives indication that customer205 did not purchase good 232, even after receiving a future rebate orprice reduction offer at step 520, then at step 530, system 100 updatesthe customer purchase behavior profile Like profile compiling step 607and model generation step 608, this updated profile step 630 may beconducted for a particular customer 205 or multiple customers 205,and/or for just one venue 200 a or multiple venues 200 b-e. At step 631,based on the updated model and customer purchasing profiles, system 100further adjusts goods 232 to remain competitive with third party retailvenues presented during the price comparison in step 609 and to furtherfollow purchasing trends at micro retail venue and customer levels, aswell as the collective macro level purchasing trends across allaffiliated retail venues 200 a-n.

While illustrative embodiments have been described herein, the scopethereof includes any and all embodiments having equivalent elements,modifications, omissions, combinations (e.g., of aspects across variousembodiments), adaptations and/or alterations as would be appreciated bythose in the art based on the present disclosure. For example, thenumber and orientation of components shown in the exemplary systems maybe modified. Thus, the foregoing description has been presented forpurposes of illustration only. It is not exhaustive and is not limitingto the precise forms or embodiments disclosed. Modifications andadaptations will be apparent to those skilled in the art fromconsideration of the specification and practice of the disclosedembodiments.

The elements in the claims are to be interpreted broadly based on thelanguage employed in the claims and not limited to examples described inthe present specification or during the prosecution of the application,which examples are to be construed as non-exclusive. It is intended,therefore, that the specification and examples be considered asexemplary only, with a true scope and spirit being indicated by thefollowing claims and their full scope of equivalents.

1. A system for determining customer interest in goods, comprising: oneor more memory devices storing instructions; and one or more processorsconfigured to execute the instructions to: receive customer locationdata from a smart device associated with a customer indicating thecustomer is within a retail venue of a retailer; monitor, based on thecustomer location data, a current location of the customer within theretail venue; receive goods location data from a plurality oftransmitter devices indicating locations of goods for sale within theretail venue; monitor, based on the goods location data, a currentlocation of the goods within the retail venue; determine that thecustomer is interested in a particular good for sale within the retailvenue based on one of the current customer location remaining inproximity to the current location of the particular good for apredetermined period of time, or a listing of the particular good withinproximity to the customer on a customer account; store eachdetermination that the customer is interested in the particular good forsale within the retail venue; generate a profile of shopping behavior ofthe customer based on the stored interest determinations; conduct asearch of pricing of the particular good at one or more other retailers;send a price comparison to the customer for the particular good based onresults of the price search; and generate a model of the customerinterest in a plurality of goods at the retail venues.
 2. (canceled) 3.The system of claim 1, the one or more processors being furtherconfigured to: receive location data of other customers within theretail venue; determine interests of the other customers in goods at theretail venue; store the interest determinations of the other customers;and generate a profile of shopping behavior of each of the othercustomers.
 4. The system of claim 3, the one or more processors beingfurther configured to: compile the generated shopping behavior profilesto generate a model of aggregate customer interest in a plurality ofproducts at the retail venue.
 5. The system of claim 1, the one or moreprocessors being further configured to: receive information indicatingwhether the customer purchases the particular good after receiving theprice comparison.
 6. The system of claim 5, wherein if the customer doesnot purchase the particular good after receiving the price comparison,the one or more processors are further configured to send to thecustomer smart device a discounted price for the good.
 7. The system ofclaim 5, if the customer does not purchase the particular good afterreceiving the price comparison, the one or more processors are furtherconfigured to determine whether to adjust a price of the particulargood.
 8. The system of claim 1, the one or more processors being furtherconfigured to: determine whether the customer decided to purchase thegood based on the monitored location of the customer and the location ofthe good.
 9. The system of claim 8, the one or more processors beingfurther configured to: generate a model that analyzes the customershopping behavior profile and purchase determination.
 10. The system ofclaim 9, the one or more processors being further configured to: send,based on the model analysis, to the customer smart device a discountedprice for the good.
 11. The system of claim 10, the one or moreprocessors being further configured to: receive information indicatingwhether the customer purchases the particular good after receiving thediscounted price; and update the customer shopping behavior profile. 12.The system of claim 1, wherein the customer includes a plurality ofcustomers and the retail venue includes a plurality of retail venues,the one or more processors being further configured to: receive customerlocation data from smart devices associated with the plurality ofcustomers at the plurality of retail venues; monitor, based on thecustomer location data, a current location of the customer within theretail venues; receive goods location data indicating locations of goodsfor sale in the retail venues; monitor, based on the goods locationdata, a current location of the goods within the retail venues;determine interests of the customers in the goods in the retail venuesbased on one of the locations of the customers remaining in proximity tothe current locations of the goods in the retail venues for apredetermined period of time, or listings of the particular goods withinproximity to the customers on the customer account; store eachdetermination that each of the customers is interested in a good forsale in one of the retail venues; and generate a shopping behaviorprofile of each of the customers based on the stored interestdeterminations.
 13. The system of claim 12, the one or more processorsbeing further configured to: compile the generated shopping behaviorprofiles for the customers to generate a model of aggregate customerinterest in a plurality of goods at the retail venues.
 14. The system ofclaim 13, wherein the plurality of retail venues are affiliated with acommon business entity.
 15. The system of claim 14, the one or moreprocessors being further configured to determine whether each of thecustomers decides to purchase the goods based on the monitored locationsof the customers and the locations of the goods.
 16. The system of claim15, wherein generating a model of aggregate customer interest includesreceiving and analyzing the determined decisions to purchase the goodsby the customers.
 17. The system of claim 16, the one or more processorsbeing further configured to receive information indicating whether thecustomers purchase the particular good after receiving the pricecomparison.
 18. The system of claim 17, wherein if any one of thecustomers does not purchase the particular good after receiving theprice comparison, the one or more processors are further configured tosend to the smart device of the non-purchasing customer a discountedprice for the good.
 19. The system of claim 18, wherein the offereddiscounted price is adjusted based on the generated model analysis. 20.The system of claim 18, if any one of the customers does not purchasethe particular good after receiving the price comparison, the one ormore processors are further configured to determine whether to adjustthe price of the particular good at the retail venue, or all affiliatedretail venues.